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Tern Client Services: Timesheet export CSV

Timesheet export CSV for Tern Client Services. 24 time entries over 2026-08-01 to 2026-08-24 across 3 projects for one client. billable_amount equals hours times hourly_rate on every row and every row is at the 100.00 standard rate. Hours total 72 and billable_amount totals 7200.00, split 1600.00 / 2400.00 / 3200.00 across the three projects.

csv

text/csv

2.5 KB
Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
24

Binary csv: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
24
Columns
11
Projects
3
Hours
72
Billable Total
7200.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Import the timesheet and recompute the billable value per project and in total.
Expected result
All 24 rows multiply out exactly. Grouping by project_id gives 16, 24 and 32 hours worth 1600.00, 2400.00 and 3200.00, which are the three invoice amounts on invoice-register.csv.

What is a .csv file?

CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.

How to use this file

Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.

How to use this file for testing

“Tern Client Services: Timesheet export CSV” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: 24 rows · 11 columns · UTF-8. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Code examples

import pandas as pd

df = pd.read_csv("timesheet-export.csv")
print(df.head())
print(df.dtypes)

Generated by generation/industry_documents.py. Free for any use, no attribution required, license.